RAG journey links

Author: HeidiSteen

articles/search/retrieval-augmented-generation-overview.md

@@ -11,7 +11,7 @@ ms.custom:
   - ignite-2023
   - ignite-2024
 ms.topic: conceptual
-ms.date: 12/18/2024
+ms.date: 04/15/2025
 ---
 
 # Retrieval Augmented Generation (RAG) in Azure AI Search
@@ -41,7 +41,7 @@ Microsoft has several built-in implementations for using Azure AI Search in a RA
 + Azure OpenAI, [use a search index with or without vectors](/azure/ai-services/openai/concepts/use-your-data).
 + Azure Machine Learning, [use a search index as a vector store in a prompt flow](/azure/machine-learning/how-to-create-vector-index).
 
-Curated approaches make it simple to get started, but for more control over the architecture, you need a custom solution. These templates create end-to-end solutions in:
+Curated approaches make it simple to get started, but for more control over the architecture, you need a custom solution. The following templates create end-to-end solutions in:
 
 + [Python](https://aka.ms/azai/py)
 + [.NET](https://aka.ms/azai/net)
@@ -58,6 +58,7 @@ A high-level summary of the pattern looks like this:
 + Send it to Azure AI Search to find relevant information.
 + Return the top ranked search results to an LLM.
 + Use the natural language understanding and reasoning capabilities of the LLM to generate a response to the initial prompt.
++ Optionally, use agentic RAG where an agent evaluates an answer and finds a better one if the original answer is incomplete or low quality.
 
 Azure AI Search provides inputs to the LLM prompt, but doesn't train the model. In RAG architecture, there's no extra training. The LLM is pretrained using public data, but it generates responses that are augmented by information from the retriever, in this case, Azure AI Search.
 
@@ -94,8 +95,6 @@ Since you probably know what kind of content you want to search over, consider t
 | text | vectors <sup>1</sup> | Text can be chunked and vectorized in an indexer pipeline, or handled externally and then [indexed as vector fields](vector-search-how-to-create-index.md) in your index. |
 | image | tokens, unaltered text <sup>2</sup> | [Skills](cognitive-search-working-with-skillsets.md) for OCR and Image Analysis can process images for text recognition or image characteristics. Image information is converted to searchable text and added to the index. Skills have an indexer requirement. |
 | image | vectors <sup>1</sup> | Images can be vectorized in an indexer pipeline, or handled externally for a mathematical representation of image content and then [indexed as vector fields](vector-search-how-to-create-index.md) in your index. You can use [Azure AI Vision multimodal](/azure/ai-services/computer-vision/how-to/image-retrieval) or an open source model like [OpenAI CLIP](https://github.com/openai/CLIP/blob/main/README.md) to vectorize text and images in the same embedding space.|
-<!-- | audio | vectors <sup>1</sup> | Vectorized audio content can be [indexed as vector fields](vector-search-how-to-create-index.md) in your index. Vectorization of audio content often requires intermediate processing that converts audio to text, and then text to vecctors. [Azure AI Speech](/azure/ai-services/speech-service/overview) and [OpenAI Whisper](https://platform.openai.com/docs/guides/speech-to-text) are two examples for this scenario. | 
-| video | vectors <sup>1</sup> | Vectorized video content can be [indexed as vector fields](vector-search-how-to-create-index.md) in your index. Similar to audio, vectorization of video content also requires extra processing, such as breaking up the video into frames or smaller chunks for vectorization. | -->
 
  <sup>1</sup> Azure AI Search provides [integrated data chunking and vectorization](vector-search-integrated-vectorization.md), but you must take a dependency on indexers and skillsets. If you can't use an indexer, Microsoft's [Semantic Kernel](/semantic-kernel/overview/) or other community offerings can help you with a full stack solution. For code samples showing both approaches, see [azure-search-vectors repo](https://github.com/Azure/azure-search-vector-samples).
 
@@ -223,39 +222,55 @@ A RAG solution that includes Azure AI Search can leverage [built-in data chunkin
 
 ## How to get started
 
+There are many ways to get started, including code-first solutions and demos.
+
+### [**Docs**](#tab/docs)
+
 + [Try this RAG quickstart](search-get-started-rag.md) for a demonstration of query integration with chat models over a search index.
 
 + [Tutorial: How to build a RAG solution in Azure AI Search](tutorial-rag-build-solution.md) for focused coverage on the features and pattern for RAG solutions that obtain grounding data from a search index.
 
-+ Start with solution accelerators:
++ [Review indexing concepts and strategies](search-what-is-an-index.md) to determine how you want to ingest and refresh data. Decide whether to use vector search, keyword search, or hybrid search. The kind of content you need to search over, and the type of queries you want to run, determines index design.
 
-  + [Chat with your data solution accelerator](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator)
-  
-  + [Conversational Knowledge Mining solution accelerator](https://github.com/microsoft/Customer-Service-Conversational-Insights-with-Azure-OpenAI-Services)
++ [Review creating queries](search-query-create.md) to learn more about search request syntax and requirements.
 
-  + [Document Knowledge Mining accelerator](https://github.com/microsoft/Document-Knowledge-Mining-Solution-Accelerator)
+> [!NOTE]
+> Some Azure AI Search features are intended for human interaction and aren't useful in a RAG pattern. Specifically, you can skip features like autocomplete and suggestions. Other features like facets and orderby might be useful, but would be uncommon in a RAG scenario.
 
-  + [Build your own copilot solution accelerator](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator)
+### [**Demos and code**](#tab/demos)
 
-    + [Client Advisor](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator/blob/main/ClientAdvisor/README.md)
+Check out the following GitHub repositories for code, documentation, and video demonstrations where applicable.
 
-    + [Research Assistant](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator/blob/main/ResearchAssistant/README.md)
++ [RAG Time Journeys](https://github.com/microsoft/rag-time)
 
-    + [Generic copilot](https://github.com/microsoft/Generic-Build-your-own-copilot-Solution-Accelerator)
++ [azure-search-vector-samples](https://github.com/Azure/azure-search-vector-samples)
 
-+ [Use enterprise chat app templates](https://aka.ms/azai) deploy Azure resources, code, and sample grounding data using fictitious health plan documents for Contoso and Northwind. This end-to-end solution gives you an operational chat app in as little as 15 minutes. Code for these templates is the **azure-search-openai-demo** featured in several presentations. The following links provide language-specific versions:
+### [**Templates**](#tab/templates)
 
-  + [.NET](https://aka.ms/azai/net)
-  + [Python](https://aka.ms/azai/py)
-  + [JavaScript](https://aka.ms/azai/js)
-  + [Java](https://aka.ms/azai/javat)
+[Use enterprise chat app templates](https://aka.ms/azai) deploy Azure resources, code, and sample grounding data using fictitious health plan documents for Contoso and Northwind. This end-to-end solution gives you an operational chat app in as little as 15 minutes. Code for these templates is the **azure-search-openai-demo** featured in several presentations. The following links provide language-specific versions:
 
-+ [Review indexing concepts and strategies](search-what-is-an-index.md) to determine how you want to ingest and refresh data. Decide whether to use vector search, keyword search, or hybrid search. The kind of content you need to search over, and the type of queries you want to run, determines index design.
++ [.NET](https://aka.ms/azai/net)
++ [Python](https://aka.ms/azai/py)
++ [JavaScript](https://aka.ms/azai/js)
++ [Java](https://aka.ms/azai/javat)
 
-+ [Review creating queries](search-query-create.md) to learn more about search request syntax and requirements.
+### [**Accelerators**](#tab/accelerators)
 
-> [!NOTE]
-> Some Azure AI Search features are intended for human interaction and aren't useful in a RAG pattern. Specifically, you can skip features like autocomplete and suggestions. Other features like facets and orderby might be useful, but would be uncommon in a RAG scenario.
++ [Chat with your data solution accelerator](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator)
+
++ [Conversational Knowledge Mining solution accelerator](https://github.com/microsoft/Customer-Service-Conversational-Insights-with-Azure-OpenAI-Services)
+
++ [Document Knowledge Mining accelerator](https://github.com/microsoft/Document-Knowledge-Mining-Solution-Accelerator)
+
++ [Build your own copilot solution accelerator](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator)
+
+  + [Client Advisor](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator/blob/main/ClientAdvisor/README.md)
+
+  + [Research Assistant](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator/blob/main/ResearchAssistant/README.md)
+
+  + [Generic copilot](https://github.com/microsoft/Generic-Build-your-own-copilot-Solution-Accelerator)
+
+---
 
 ## See also
 

articles/search/whats-new.md

@@ -7,7 +7,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: overview
-ms.date: 03/31/2025
+ms.date: 04/15/2025
 ms.custom:
   - references_regions
   - ignite-2024
@@ -20,6 +20,12 @@ Learn about the latest updates to Azure AI Search functionality, docs, and sampl
 > [!NOTE]
 > Preview features are announced here, but we also maintain a [preview features list](search-api-preview.md) so you can find them in one place.
 
+## April 2025
+
+| Item                         | Type |  Description |
+|-----------------------------|------|--------------|
+| [RAG Time Journey](https://github.com/microsoft/rag-time) | Demo code | Code and video demonstrations of Retrieval Augmented Generation (RAG) workflows that use Azure AI Search. Segments include fundamentals, patterns and use-cases, [vector indexing at scale](https://github.com/microsoft/rag-time/tree/main/Journey%203%20-%20Optimize%20your%20Vector%20Index%20for%20Scale), and [agentic search](https://github.com/microsoft/rag-time/tree/main/Journey%20Bonus%20-%20Agentic%20RAG) where you use an agent to evaluate a result and generate a better answer. |
+
 ## March 2025
 
 | Item                         | Type |  Description |